Network Analysis and Causal Inference Through Structural Equation Modeling

Estimate networks and causal relationships in complex systems through Structural Equation Modeling. This package also includes functions for importing, weight, manipulate, and fit biological network models within the Structural Equation Modeling framework as outlined in the Supplementary Material of Grassi M, Palluzzi F, Tarantino B (2022) .

SEMgraph

Overview

SEMgraph Estimate causal relations in network or in complex systems with Structural Equation Modeling (SEM) using as input a directed graph that encodes the hypothesized or data-driven causal relationships among variables, a data matrix with n samples and p variables, and (optional) a binary group vector of experimental conditions for the n samples. SEMgraph comes with the following functionalities:

• Interchangeable model representation as either an igraph object or the corresponding SEM in lavaan syntax. Model management functions include graph-to-SEM conversion, automated covariance matrix regularization, graph conversion to DAG (Directed Acyclic Graph), and tree (arborescence) from correlation matrices.
• Heuristic filtering, node and edge weighting, resampling and parallelization settings for fast fitting in case of very large models.
• Automated data-driven model building and improvement, through causal structure learning and bow-free interaction search and latent variable confounding adjustment.
• Perturbed paths finding, community searching and sample scoring, together with graph plotting utilities, tracing model architecture modifications and perturbation (i.e., activation or repression) routes.

Installation

The latest stable version can be installed from CRAN:

install.packages("SEMgraph")

The latest development version can be installed from GitHub:

devtools::install_github("fernandoPalluzzi/SEMgraph")

Getting Started

The full list of SEMgraph functions with examples and a tutorial is available HERE.  

References

Grassi M, Palluzzi F, Tarantino B. SEMgraph: an R package for causal network inference of high-throughput data with structural equation models. Bioinformatics, 2022 Aug 30; 38(20):btac567. https://doi.org/10.1093/bioinformatics/btac567

Grassi M, Tarantino B. SEMgsa: topology-based pathway enrichment analysis with structural equation models. BMC Bioinformatics, 2022 Aug 17; 23(1):344. https://doi.org/10.1186/s12859-022-04884-8

Grassi M, Tarantino B. SEMtree: tree-based structure learning methods with structural equation models. Bioinformatics, 2023 June 09; 39(6):btad377. https://doi.org/10.1093/bioinformatics/btad377

Grassi M, Tarantino B. SEMbap: Bow-free covariance search and data de-correlation. PLoS Comput Biol, 2024 Sep 11; 20(9):e1012448. https://doi.org/10.1371/journal.pcbi.1012448

Grassi M, Tarantino B. SEMdag: Fast learning of Directed Acyclic Graphs via node or layer ordering. PLoS ONE. 2025 Jan 08; 20(1): e0317283. https://doi.org/10.1371/journal.pone.0317283